crazyara.py

"""
@file: crazyara.py
Created on 18.10.18
@project: crazy_ara_refactor
@author: queensgambit

Universal chess interface (CUI) communication protocol interface for the engine.
The protocol was published by Stefan-Meyer Kahlen (ShredderChess) and can be accessed at:
http://wbec-ridderkerk.nl/html/UCIProtocol.html
"""

from __future__ import print_function
import collections
import sys
import traceback
import chess.variant
import numpy as np
import multiprocessing

from DeepCrazyhouse.src.domain.agent.player.alpha_beta_agent import AlphaBetaAgent
from DeepCrazyhouse.src.runtime.color_logger import enable_color_logging


class CrazyAra:  # Too many instance attributes (25/7)
    """Main"""

    def __init__(self):
        enable_color_logging()
        # Constants
        self.min_search_time = 100
        self.max_search_time = 10e10
        self.inc_factor = 7
        self.inc_div = 8
        self.min_moves_left = self.moves_left_increment = 10  # Used to reduce the movetime in the opening
        self.max_bad_pos_value = -0.10  # When pos eval [-1.0 to 1.0] is equal or worst than this then extend time
        # this is the assumed "maximum" blitz game length for calculating a constant movetime
        # after 80% of this game length a new time management starts which is based on movetime left
        self.blitz_game_length = 50
        # use less time in the opening defined by "max_move_num_to_reduce_movetime" by using the constant move time
        self.mv_time_opening_portion = 0.7
        # this variable is intended to increase variance in the moves played by using a different amount of time each
        # move
        self.random_mv_time_portion = 0.1
        # enable this variable if you want to see debug messages in certain environments, like the lichess.org api
        self.enable_lichess_debug_msg = self.setup_done = False
        self.client = {"name": "CrazyAra", "version": "0.5.1", "authors": "Johannes Czech, Moritz Willig, Alena Beyer"}
        self.mcts_agent = (
            self.rawnet_agent
        ) = self.ab_agent = self.gamestate = self.bestmove_value = self.move_time = self.score = None
        self.engine_played_move = 0
        self.log_file_path = "CrazyAra-log.txt"
        self.score_file_path = "score-log.txt"
        self.settings = {
            "UCI_Variant": "crazyhouse",
            "search_type": "mcts",  # mcts, alpha_beta
            "ab_depth": 5,  # depth to reach for alpha_beta
            "ab_candidate_moves": 7,  # candidate moves to consider for ab-search, clipped according to NN policy
            # set the context in which the neural networks calculation will be done
            # choose 'gpu' using the settings if there is one available
            "context": "cpu",
            "use_raw_network": False,
            "threads": min(8, multiprocessing.cpu_count()),
            "batch_size": 8,
            "neural_net_services": 1,
            "playouts_empty_pockets": 99999,
            "playouts_filled_pockets": 99999,
            "centi_cpuct": 250,
            "centi_dirichlet_epsilon": 25,
            "centi_dirichlet_alpha": 20,
            "centi_u_init_divisor": 100,
            "max_search_depth": 99,
            "centi_temperature": 7,
            "temperature_moves": 0,
            "opening_guard_moves": 0,
            "centi_clip_quantil": 0,
            "virtual_loss": 3,
            "centi_q_value_weight": 70,
            "threshold_time_for_raw_net_ms": 100,
            "move_overhead_ms": 300,
            "moves_left": 40,
            "extend_time_on_bad_position": True,
            "max_move_num_to_reduce_movetime": 4,
            "enhance_checks": False,
            "enhance_captures": False,
            "use_pruning": False,
            "use_future_q_values": False,
            "use_time_management": True,
            "use_transposition_table": True,
            "verbose": False,
            "model_architecture_dir": "default",
            "model_weights_dir": "default"
        }
        self.cmd_list = []
        try:
            self.log_file = open(self.log_file_path, "w")
        except IOError:
            self.log_file = None
            # print out the error message
            print("info string An error occurred while trying to open the self.log_file %s" % self.log_file_path)
            traceback.print_exc()

        self.intro = """\
                                  _                                           
                   _..           /   ._   _.  _        /\   ._   _.           
                 .' _ `\         \_  |   (_|  /_  \/  /--\  |   (_|           
                /  /e)-,\                         /                           
               /  |  ,_ |                    __    __    __    __             
              /   '-(-.)/          bw     8 /__////__////__////__////         
            .'--.   \  `                 7 ////__////__////__////__/          
           /    `\   |                  6 /__////__////__////__////           
         /`       |  / /`\.-.          5 ////__////__////__////__/            
       .'        ;  /  \_/__/         4 /__////__////__////__////             
     .'`-'_     /_.'))).-` \         3 ////__////__////__////__/              
    / -'_.'---;`'-))).-'`\_/        2 /__////__////__////__////        
   (__.'/   /` .'`                 1 ////__////__////__////__/                
    (_.'/ /` /`                       a  b  c  d  e  f  g  h                  
      _|.' /`                                                                 
jgs.-` __.'|  Developers: Johannes Czech, Moritz Willig, Alena Beyer          
    .-'||  |  Source-Code: QueensGambit/CrazyAra (GPLv3-License)              
       \_`/   Inspiration: A0-paper by Silver, Hubert, Schrittwieser et al.  
              ASCII-Art: Joan G. Stark, Chappell, Burton                      """

    @staticmethod
    def eprint(*args, **kwargs):
        """ Wrapper of print() using stderr"""
        print(*args, file=sys.stderr, **kwargs)

    def print_if_debug(self, string):
        """ Print lichess debug message on the log"""
        if self.enable_lichess_debug_msg:
            self.eprint("[debug] " + string)

    def log_print(self, text: str):
        """ Print all log messages on the log file"""
        print(text)
        self.print_if_debug(text)
        self.log(text)

    def write_score_to_file(self, score: str):
        """Send the score to score.txt"""
        with open(self.score_file_path, "w") as selected_file:
            selected_file.seek(0)
            selected_file.write(score)
            selected_file.truncate()

    def log(self, text: str):
        """ Sends the text to the log file"""
        if self.log_file:
            self.log_file.write("> %s\n" % text)
            self.log_file.flush()

    def setup_network(self):
        """
        Load the libraries and the weights of the neural network
        :return:
        """
        if not self.setup_done:
            from DeepCrazyhouse.src.domain.variants.game_state import GameState
            from DeepCrazyhouse.src.domain.agent.neural_net_api import NeuralNetAPI
            from DeepCrazyhouse.src.domain.agent.player.raw_net_agent import RawNetAgent
            from DeepCrazyhouse.src.domain.agent.player.mcts_agent import MCTSAgent

            self.param_validity_check()  # check for valid parameter setup and do auto-corrections if possible

            nets = []
            for _ in range(self.settings["neural_net_services"]):
                nets.append(NeuralNetAPI(ctx=self.settings["context"], batch_size=self.settings["batch_size"],
                                         model_architecture_dir=self.settings["model_architecture_dir"],
                                         model_weights_dir=self.settings["model_weights_dir"]))

            self.rawnet_agent = RawNetAgent(
                nets[0],
                temperature=self.settings["centi_temperature"] / 100,
                temperature_moves=self.settings["temperature_moves"],
            )

            self.mcts_agent = MCTSAgent(
                nets,
                cpuct=self.settings["centi_cpuct"] / 100,
                playouts_empty_pockets=self.settings["playouts_empty_pockets"],
                playouts_filled_pockets=self.settings["playouts_filled_pockets"],
                max_search_depth=self.settings["max_search_depth"],
                dirichlet_alpha=self.settings["centi_dirichlet_alpha"] / 100,
                q_value_weight=self.settings["centi_q_value_weight"] / 100,
                dirichlet_epsilon=self.settings["centi_dirichlet_epsilon"] / 100,
                virtual_loss=self.settings["virtual_loss"],
                threads=self.settings["threads"],
                temperature=self.settings["centi_temperature"] / 100,
                temperature_moves=self.settings["temperature_moves"],
                verbose=self.settings["verbose"],
                min_movetime=self.min_search_time,
                batch_size=self.settings["batch_size"],
                enhance_checks=self.settings["enhance_checks"],
                enhance_captures=self.settings["enhance_captures"],
                use_future_q_values=self.settings["use_future_q_values"],
                use_pruning=self.settings["use_pruning"],
                use_time_management=self.settings["use_time_management"],
                use_transposition_table=self.settings["use_transposition_table"],
                opening_guard_moves=self.settings["opening_guard_moves"],
                u_init_divisor=self.settings["centi_u_init_divisor"] / 100,
            )

            self.ab_agent = AlphaBetaAgent(
                nets[0],
                depth=self.settings["ab_depth"],
                nb_candidate_moves=self.settings["ab_candidate_moves"],
                include_check_moves=False,
            )

            if self.settings["UCI_Variant"] == "crazyhouse":
                board = chess.variant.CrazyhouseBoard()
            elif self.settings["UCI_Variant"] == "giveaway":
                board = chess.variant.GiveawayBoard()
            else:
                raise Exception("Variant %s is not supported yet" % self.settings["UCI_Variant"])

            self.gamestate = GameState(board)
            self.setup_done = True

    def validity_with_threads(self, optname: str):
        """
        Checks for consistency with the number of threads with the given parameter
        :param optname: Option name
        :return:
        """

        if self.settings[optname] > self.settings["threads"]:
            self.log_print(
                "info string The given batch_size %d is higher than the number of threads %d. "
                "The maximum legal batch_size is the same as the number of threads (here: %d) "
                % (self.settings[optname], self.settings["threads"], self.settings["threads"])
            )
            self.settings[optname] = self.settings["threads"]
            self.log_print("info string The batch_size was reduced to %d" % self.settings[optname])

        if self.settings["threads"] % self.settings[optname] != 0:
            self.log_print(
                "info string You requested an illegal combination of threads %d and batch_size %d."
                " The batch_size must be a divisor of the number of threads"
                % (self.settings["threads"], self.settings[optname])
            )
            divisor = self.settings["threads"] // self.settings[optname]
            self.settings[optname] = self.settings["threads"] // divisor
            self.log_print("info string The batch_size was changed to %d" % self.settings[optname])

    def param_validity_check(self):
        """
        Handles some possible issues when giving an illegal batch_size and number of threads combination.
        :return:
        """

        self.validity_with_threads("batch_size")
        self.validity_with_threads("neural_net_services")

    def _get_wtime_btime_idx(self):
        """
        Return the movement time for white and black in the command list.
        Assumes that cmd list has at least a length of 4.
        :return:
        """
        
        wtime_idx = None
        btime_idx = None

        # wtime and btime can be sent in an arbitrary order
        if self.cmd_list[1] == "wtime":
            wtime_idx = 1
        elif self.cmd_list[3] == "wtime":
            wtime_idx = 3

        if self.cmd_list[1] == "btime":
            btime_idx = 1
        elif self.cmd_list[3] == "btime":
            btime_idx = 3
            
        return wtime_idx, btime_idx
            
    def _get_movetime_5_args(self):
        """
        Returns the movetime when given 5 command line arguments
        :return: movetime in ms
        """

        wtime_idx, btime_idx = self._get_wtime_btime_idx()

        if wtime_idx and btime_idx:
            wtime = int(self.cmd_list[wtime_idx + 1])
            btime = int(self.cmd_list[btime_idx + 1])

            winc = binc = 0
            if "winc" in self.cmd_list:
                winc = int(self.cmd_list[wtime_idx + 5])
            if "binc" in self.cmd_list:
                binc = int(self.cmd_list[btime_idx + 5])

            if self.gamestate.is_white_to_move():
                my_time = wtime
                my_inc = winc
            else:
                my_time = btime
                my_inc = binc

            if self.move_time is None:
                self.move_time = (my_time + self.blitz_game_length * my_inc) / self.blitz_game_length

            # TC with period (traditional) like 40/60 or 40 moves in 60 sec repeating
            if "movestogo" in self.cmd_list:
                tc_type = "traditional"
                if "winc" in self.cmd_list and "binc" in self.cmd_list:
                    moves_left = int(self.cmd_list[10])
                else:
                    moves_left = int(self.cmd_list[6])
                # If we are close to the period limit, save extra time to avoid time forfeit
                if moves_left <= 3:
                    moves_left += 1
            else:
                tc_type = "blitz"
                moves_left = self.settings["moves_left"]

            moves_left = self.adjust_moves_left(moves_left, tc_type, self.bestmove_value)
            if tc_type == "blitz" and self.engine_played_move < self.blitz_game_length * 0.8:
                movetime_ms = (
                        self.move_time + (np.random.rand() - 0.5) * self.random_mv_time_portion * self.move_time
                )

                if self.engine_played_move < self.settings["max_move_num_to_reduce_movetime"]:
                    # avoid spending too much time in the opening
                    movetime_ms *= self.mv_time_opening_portion
            else:
                movetime_ms = max(
                    my_time / moves_left
                    + self.inc_factor * my_inc // self.inc_div
                    - self.settings["move_overhead_ms"],
                    self.min_search_time,
                )
        else:
            # set the minimum search time as the default value
            movetime_ms = self.min_search_time
                
        return movetime_ms
        
    def get_movetime(self):
        """
        Calculates the movetime given the command-line arguments.
        :return:
        """
        
        # set the minimum search time as the default value
        movetime_ms = self.min_search_time
        
        if len(self.cmd_list) >= 5:
            movetime_ms = self._get_movetime_5_args()
            
        # movetime in UCI protocol, go movetime x, search exactly x ms
        # UCI protocol: http://wbec-ridderkerk.nl/html/UCIProtocol.html
        elif len(self.cmd_list) == 3 and self.cmd_list[1] == "movetime":
            movetime_ms = max(int(self.cmd_list[2]) - self.settings["move_overhead_ms"], self.min_search_time)

        return movetime_ms
    
    def perform_action(self):  # Probably needs refactoring
        """
        Computes the 'best move' according to the engine and the given settings.
        After the search is done it will print out ' bestmove e2e4' for example on std-out.
        :return:
        """

        if self.gamestate.is_variant_end():
            self.log_print("info string The requested position %s doesn't have any legal move." % self.gamestate)
            return

        movetime_ms = self.get_movetime()

        self.mcts_agent.update_movetime(movetime_ms)
        self.log_print("info string Time for this move is %dms" % movetime_ms)
        self.log_print("info string Requested pos: %s" % self.gamestate)

        # assign search depth
        try:
            # we try to extract the search depth from the cmd list
            self.mcts_agent.set_max_search_depth(int(self.cmd_list[self.cmd_list.index("depth") + 1]))
            movetime_ms = self.max_search_time  # increase the movetime to maximum to make sure to reach the given depth
            self.mcts_agent.update_movetime(movetime_ms)
        except ValueError:
            pass  # the given command wasn't found in the command list

        # disable noise for short move times
        if movetime_ms < 1000:
            self.mcts_agent.dirichlet_epsilon = 0.1
        elif movetime_ms < 7000:
            # reduce noise for very short move times
            self.mcts_agent.dirichlet_epsilon = 0.2

        if self.settings["search_type"] == "alpha_beta":
            value, selected_move, _, _, centipawn, depth, nodes, time_elapsed_s, nps, pv = self.ab_agent.perform_action(
                self.gamestate
            )
        elif self.settings["search_type"] == "mcts":
            if self.settings["use_raw_network"] or movetime_ms <= self.settings["threshold_time_for_raw_net_ms"]:
                self.log_print("info string Using raw network for fast mode...")
                value, selected_move, _, _, centipawn, depth, nodes, time_elapsed_s, nps, pv = self.rawnet_agent.perform_action(
                    self.gamestate
                )
            else:
                value, selected_move, _, _, centipawn, depth, nodes, time_elapsed_s, nps, pv = self.mcts_agent.perform_action(
                    self.gamestate
                )
        else:
            raise Exception("Unknown search type %s" % self.settings["search_type"])

        self.score = "score cp %d depth %d nodes %d time %d nps %d pv %s" % (
            centipawn,
            depth,
            nodes,
            time_elapsed_s,
            nps,
            pv,
        )
        if self.enable_lichess_debug_msg:
            try:
                self.write_score_to_file(self.score)
            except IOError:
                traceback.print_exc()

        self.log_print("info %s" % self.score)  # print out the search information
        # Save the bestmove value [-1.0 to 1.0] to modify the next movetime
        self.bestmove_value = float(value)
        self.engine_played_move += 1

        # apply CrazyAra's selected move the global gamestate
        if self.gamestate.get_pythonchess_board().is_legal(selected_move):
            # apply the last move CrazyAra played
            self._apply_move(selected_move)
        else:
            raise Exception("all_ok is false! - crazyara_last_move")

        self.log_print("bestmove %s" % selected_move.uci())

    def setup_gamestate(self):  # Too many branches (13/12)
        """
        Prepare the gamestate according to the user's wishes.

        :param self.cmd_list: Input-command lists arguments
        :return:
        """
        position_type = self.cmd_list[1]

        if "moves" in self.cmd_list:
            # position startpos moves e2e4 g8f6
            if position_type == "startpos":
                mv_list = self.cmd_list[3:]
            else:
                # position fen rn2N2k/pp5p/3pp1pN/3p4/3q1P2/3P1p2/PP3PPP/RN3RK1/Qrbbpbb b - - 3 27 moves d4f2 f1f2
                mv_list = self.cmd_list[9:]

            # try to apply opponent last move to the board state
            if mv_list:
                # the move the opponent just played is the last move in the list
                opponent_last_move = chess.Move.from_uci(mv_list[-1])
                if self.gamestate.get_pythonchess_board().is_legal(opponent_last_move):
                    # apply the last move the opponent played
                    self._apply_move(opponent_last_move)
                    mv_compatible = True
                else:
                    self.log_print("info string  all_ok is false! - opponent_last_move %s" % opponent_last_move)
                    mv_compatible = False
            else:
                mv_compatible = False

            if not mv_compatible:
                self.log_print("info string The given last two moves couldn't be applied to the previous board-state.")
                self.log_print("info string Rebuilding the game from scratch...")

                # create a new game state from scratch
                if position_type == "startpos":
                    self.new_game()
                else:
                    fen = " ".join(self.cmd_list[2:8])
                    self.gamestate.set_fen(fen)

                for move in mv_list:
                    self._apply_move(chess.Move.from_uci(move))
            else:
                self.log_print("info string Move Compatible")
        else:
            if position_type == "fen":
                fen = " ".join(self.cmd_list[2:8])
                self.gamestate.set_fen(fen)
                self.mcts_agent.update_transposition_table((self.gamestate.get_transposition_key(),))
                # log_print("info string Added %s - count %d" % (gamestate.get_board_fen(),
                #                                    mcts_agent.transposition_table[gamestate.get_transposition_key()]))

    def _apply_move(self, selected_move: chess.Move):
        """
        Applies the given move on the gamestate and updates the transposition table of the environment
        :param selected_move: Move in python chess format
        :return:
        """

        self.gamestate.apply_move(selected_move)
        self.mcts_agent.update_transposition_table((self.gamestate.get_transposition_key(),))
        # log_print("info string Added %s - count %d" % (gamestate.get_board_fen(),
        #                                               mcts_agent.transposition_table[
        #                                                   gamestate.get_transposition_key()]))

    def new_game(self):
        """Group everything related to start the game"""
        self.log_print("info string >> New Game")
        self.gamestate.new_game()
        self.mcts_agent.transposition_table = collections.Counter()
        self.mcts_agent.time_buffer_ms = 0
        self.mcts_agent.dirichlet_epsilon = self.settings["centi_dirichlet_epsilon"] / 100

    def set_options(self):  # Too many branches (16/12)
        """
        Updates the internal options as requested by the use via the uci-protocol
        An example call could be: "setoption name nb_threads value 1"
        :param self.cmd_list: List of received of commands
        :return:
        """
        # make sure there exists enough items in the given command list like "setoption name nb_threads value 1"
        if len(self.cmd_list) >= 5:
            if self.cmd_list[1] != "name" or self.cmd_list[3] != "value":
                self.log_print("info string The given setoption command wasn't understood")
                self.log_print('info string An example call could be: "setoption name threads value 4"')
            else:
                option_name = self.cmd_list[2]

                if option_name not in self.settings:
                    self.log_print("info string The given option %s wasn't found in the settings list" % option_name)
                else:

                    if option_name in [
                        "UCI_Variant",
                        "search_type",
                        "context",
                        "use_raw_network",
                        "extend_time_on_bad_position",
                        "verbose",
                        "enhance_checks",
                        "enhance_captures",
                        "use_pruning",
                        "use_future_q_values",
                        "use_time_management",
                        "use_transposition_table",
                        "model_architecture_dir",
                        "model_weights_dir",
                    ]:
                        value = self.cmd_list[4]
                    else:
                        value = int(self.cmd_list[4])

                    if option_name == "use_raw_network":
                        self.settings["use_raw_network"] = value == "true"
                    elif option_name == "extend_time_on_bad_position":
                        self.settings["extend_time_on_bad_position"] = value == "true"
                    elif option_name == "verbose":
                        self.settings["verbose"] = value == "true"
                    elif option_name == "enhance_checks":
                        self.settings["enhance_checks"] = value == "true"
                    elif option_name == "enhance_captures":
                        self.settings["enhance_captures"] = value == "true"
                    elif option_name == "use_pruning":
                        self.settings["use_pruning"] = value == "true"
                    elif option_name == "use_future_q_values":
                        self.settings["use_future_q_values"] = value == "true"
                    elif option_name == "use_time_management":
                        self.settings["use_time_management"] = value == "true"
                    elif option_name == "use_transposition_table":
                        self.settings["use_transposition_table"] = value == "true"
                    else:
                        self.settings[option_name] = value  # by default all options are treated as integers
                        # Guard threads limits
                        if option_name == "threads":
                            self.settings[option_name] = min(4096, max(1, self.settings[option_name]))

                    self.log_print("info string Updated option %s to %s" % (option_name, value))

    def adjust_moves_left(self, moves_left, tc_type, prev_bm_value):
        """
        We can reduce the movetime early in the opening as the NN may be able to handle it well.
        Or when the position is bad we can increase the movetime especially if there are enough time left.
        To increase/decrease the movetime, we decrease/increase the moves_left.
        movetime = time_left/moves_left
        :param moves_left: Moves left for the next period for traditional or look ahead moves for blitz
        :param tc_type: Can be blitz (60+1) or traditional (40/60)
        :param prev_bm_value: The value of the previous bestmove. value is in the range [-1 to 1]
        :return: moves_left
        """

        # Don't spend too much time in the opening, we increase the moves_left
        # so that the movetime is reduced. engine_played_move is the actual moves
        # made by the engine excluding the book moves input from a GUI.
        if self.engine_played_move < self.settings["max_move_num_to_reduce_movetime"]:
            moves_left += self.moves_left_increment

        # Increase movetime by reducing the moves left if our prev bestmove value is below 0.0
        elif self.settings["extend_time_on_bad_position"] and prev_bm_value and prev_bm_value <= self.max_bad_pos_value:
            if tc_type == "blitz":
                # The more the bad position is, the more that we extend the search time
                moves_left -= abs(prev_bm_value) * self.settings["moves_left"]
                moves_left = max(moves_left, self.min_moves_left)
            # Else if TC is traditional, we extend with more time if we have more time left
            elif moves_left > 4:
                moves_left -= moves_left // 8

        return moves_left

    def uci_reply(self):
        """Group UCI log info's"""
        self.log_print("id name %s %s" % (self.client["name"], self.client["version"]))
        self.log_print("id author %s" % self.client["authors"])
        # tell the GUI all possible options
        self.log_print("option name UCI_Variant type combo default crazyhouse var crazyhouse")
        self.log_print(
            "option name search_type type combo default %s var mcts var alpha_beta" % self.settings["search_type"]
        )
        self.log_print("option name ab_depth type spin default %d min 1 max 40" % self.settings["ab_depth"])
        self.log_print(
            "option name ab_candidate_moves type spin default %d min 1 max 4096" % self.settings["ab_candidate_moves"]
        )
        self.log_print("option name context type combo default %s var cpu var gpu" % self.settings["context"])
        self.log_print(
            "option name use_raw_network type check default %s"
            % ("false" if not self.settings["use_raw_network"] else "true")
        )
        self.log_print("option name threads type spin default %d min 1 max 4096" % self.settings["threads"])
        self.log_print("option name batch_size type spin default %d min 1 max 4096" % self.settings["batch_size"])
        self.log_print(
            "option name neural_net_services type spin default %d min 1 max 10" % self.settings["neural_net_services"]
        )
        self.log_print(
            "option name playouts_empty_pockets type spin default %d min 56 max 99999"
            % self.settings["playouts_empty_pockets"]
        )
        self.log_print(
            "option name playouts_filled_pockets type spin default %d min 56 max 99999"
            % self.settings["playouts_filled_pockets"]
        )
        self.log_print("option name centi_cpuct type spin default %d min 1 max 500" % self.settings["centi_cpuct"])
        self.log_print(
            "option name centi_dirichlet_epsilon type spin default %d min 0 max 100"
            % self.settings["centi_dirichlet_epsilon"]
        )
        self.log_print(
            "option name centi_dirichlet_alpha type spin default %d min 0 max 100"
            % self.settings["centi_dirichlet_alpha"]
        )
        self.log_print(
            "option name centi_u_init_divisor type spin default %d min 1 max 100"
            % self.settings["centi_u_init_divisor"]
        )
        self.log_print(
            "option name max_search_depth type spin default %d min 1 max 100" % self.settings["max_search_depth"]
        )
        self.log_print(
            "option name centi_temperature type spin default %d min 0 max 100" % self.settings["centi_temperature"]
        )
        self.log_print(
            "option name temperature_moves type spin default %d min 0 max 99999" % self.settings["temperature_moves"]
        )
        self.log_print(
            "option name opening_guard_moves type spin default %d min 0 max 99999"
            % self.settings["opening_guard_moves"]
        )
        self.log_print("option name centi_clip_quantil type spin default 0 min 0 max 100")
        self.log_print("option name virtual_loss type spin default 3 min 0 max 10")
        self.log_print(
            "option name centi_q_value_weight type spin default %d min 0 max 100"
            % self.settings["centi_q_value_weight"]
        )
        self.log_print(
            "option name threshold_time_for_raw_net_ms type spin default %d min 1 max 300000"
            % self.settings["threshold_time_for_raw_net_ms"]
        )
        self.log_print(
            "option name move_overhead_ms type spin default %d min 0 max 60000" % self.settings["move_overhead_ms"]
        )
        self.log_print("option name moves_left type spin default %d min 10 max 320" % self.settings["moves_left"])
        self.log_print(
            "option name extend_time_on_bad_position type check default %s"
            % ("false" if not self.settings["extend_time_on_bad_position"] else "true")
        )
        self.log_print(
            "option name max_move_num_to_reduce_movetime type spin default %d min 0 max 120"
            % self.settings["max_move_num_to_reduce_movetime"]
        )
        self.log_print(
            "option name enhance_checks type check default %s"
            % ("false" if not self.settings["enhance_checks"] else "true")
        )
        self.log_print(
            "option name enhance_captures type check default %s"
            % ("false" if not self.settings["enhance_captures"] else "true")
        )
        self.log_print(
            "option name use_pruning type check default %s" % ("false" if not self.settings["use_pruning"] else "true")
        )
        self.log_print(
            "option name use_future_q_values type check default %s"
            % ("false" if not self.settings["use_future_q_values"] else "true")
        )
        self.log_print(
            "option name use_time_management type check default %s"
            % ("false" if not self.settings["use_time_management"] else "true")
        )
        self.log_print(
            "option name use_transposition_table type check default %s"
            % ("false" if not self.settings["use_transposition_table"] else "true")
        )
        self.log_print(
            "option name verbose type check default %s" % ("false" if not self.settings["verbose"] else "true")
        )
        self.log_print(
            "option name model_architecture_dir type string default %s"
            % self.settings["model_architecture_dir"]
        )
        self.log_print(
            "option name model_weights_dir type string default %s"
            % self.settings["model_weights_dir"]
        )
        self.log_print("uciok")  # verify that all options have been sent

    def main(self):
        """ Main waiting loop for processing command line inputs"""
        self.eprint(self.intro)
        while True:
            line = input()
            self.print_if_debug("waiting ...")
            self.print_if_debug(line)
            # wait for an std-in input command
            if line:
                self.cmd_list = line.rstrip().split(" ")  # split the line to a list which makes parsing easier
                main_cmd = self.cmd_list[0]  # extract the first command from the list for evaluation
                self.log(line)  # write the given command to the log-file

                try:
                    if main_cmd == "uci":
                        self.uci_reply()
                    elif main_cmd == "isready":
                        self.setup_network()
                        self.log_print("readyok")
                    elif main_cmd == "ucinewgame":
                        self.bestmove_value = None
                        self.engine_played_move = 0
                        self.new_game()
                    elif main_cmd == "position":
                        self.setup_gamestate()
                    elif main_cmd == "setoption":
                        self.set_options()
                    elif main_cmd == "go":
                        self.perform_action()
                    elif main_cmd in ("quit", "exit"):
                        if self.log_file:
                            self.log_file.close()
                        return 0
                    else:
                        # give the user a message that the command was ignored
                        print("info string Unknown command: %s" % line)
                except Exception:  # all possible exceptions
                    # log the error message to the log-file and exit the script
                    traceback_text = traceback.format_exc()
                    self.log_print(traceback_text)
                    return -1


if __name__ == "__main__":
    CrazyAra.main(CrazyAra())